The etiology of many complex diseases, including Type-1 Diabetes (T1D), cannot be simply explained by genetic causes. Various factors, genetic as well as environmental, influence the progress of diseases. The critical issue to deriving the full benefit from biological, clinical, and longitudinal cohort studies for complex diseases is the appropriate analysis of the available large volumes of data, including these large-scale measurements and knowledge accrued from past research. Data mining approaches, especially feature selection from the massive number of measurements, become critical to identify reproducible and accurate risk factors to characterize pathogenic processes or pharmacologic responses to a therapeutic intervention for complex diseases including T1D. At the same time, data collection takes a significant amount of time and resources. Identifying risk factors and their interactions will significantly expedite the research at a low cost. The pri- mary objective of the proposed application is to develop a general network-based mathematical framework and efficient algorithms for identifying risk factors and their interactions as prognostic features that are highly informative about disease development. We will apply the developed algorithms to analyze the existing large-scale studies maintained at the Pediatric Epidemiology Center (PEC) at the University of South Florida (USF), including The Environmental Determinants of Diabetes in the Young (TEDDY) and the Diabetes Prevention Trial-Type 1 (DPT-1) studies. The identification of risk factors and their interactions provides deep insights to disease causality and mechanism. The proposed project has three specific aims: (1) An innovative data-driven analysis framework for risk factor identification will be presented and a general network-based mathematical model to identify risk factors and their interactions for disease development will be developed. (2) Fast and effective risk factor identification algorithms will be developed, which can be used to identify accurate synergistic factors. (3) The developed algorithms will be used for the large-scale studies, including TEDDY and DPT-1, to identify both genetic and environmental risk factors and their interactions with high predictive values for T1D development. We also will evaluate the performance of our algorithms in comparison with other traditional analysis for predicting the development and onset of T1D. Upon successful completion of this project, we expect that the developed algorithms will become a useful tool for biomedical data analysis with significant impacts on patient-oriented research for understanding the etiology, incidence, prevalence, natural history, and pathophysiology of T1D and other complex diseases. The proposed application will lay down the foundation and provide the direction for exploratory research on the re-use and analysis of existing data sets and the development of novel hypothesis and experiment design. 1